If we fix a domain and assume availability of a training set drawn from the domain, we may be able to learn a domain-specific mechanism for finding salient and meaningful correspondences. This can be done through facial landmark detection [1]} or through learning a data-driven detector for relevant points [2]}, [3]}. Alternatively, we could expect a user interacting with a style transfer tool to manually select points they consider matching in the two images. If matching points are provided by such approaches, they can be used in DST as we show in Figure REF . However, we were interested in a more general scenario, a one-shot, domain-agnostic setting where we may not have access to such points. In this setting we turned to Neural Best Buddies (NBB), a generic method for point matching between images.
